Large Language Model Prompt Datasets: An In-depth Analysis and Insights

Large Language Model Prompt Datasets: An In-depth Analysis and Insights

Large Language Model Prompt Datasets: An In-depth Analysis and Insights

Yuanming Zhang*, Yan Lin*, Arijit Khan†, Huaiyu Wan

Beijing Jiaotong University, Aalborg University, Bowling Green State University

October 10, 2025

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Abstract

A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering.

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Data Collection

Comprehensive collection of 1.22 TB of data, comprising 673M+ prompt instances from 129 heterogeneous sources:

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Dataset Platforms

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Academic Publications

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Public Repositories

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Social Media

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Taxonomy

Hierarchical categorization of LLM prompt datasets by:

Downstream Tasks

Languages

Engineering Techniques

Attributes

Modalities

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Analysis Methodology

Multi-level linguistic analysis across three dimensions on seven representative datasets:

Lexical

Token distribution, vocabulary analysis

Syntactic

Dependency parsing, POS tagging, TF-IDF

Semantic

Topic modeling, semantic similarity

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Key Findings

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Prompts exhibit distinct compositional patterns compared to other text corpora

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Domain-specific variations in prompt construction across different applications

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Unique linguistic properties distinguish prompts from literature and web content

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Prompts tend to be more directive and task-oriented than general text

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Optimization Approach

Novel prompt optimization method leveraging syntactic embeddings:

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Extract POS & Dependency Features

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Identify Centroid Representation

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Guide LLMs to Rewrite Prompts

Results in improved meaningfulness and quality of model outputs.

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Impact and Applications

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First comprehensive compilation of prompt datasets

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Provides foundation for systematic prompt engineering research

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Enables more effective prompt selection and refinement

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Facilitates broader LLM deployment across diverse applications

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Resources

Datasets and code available for research use:

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https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416

Over 1.22 TB of curated prompt data for research use